Field
The disclosure relates generally to the field of measuring similarities, and more specifically to systems, methods, and devices for measuring similarities of and generating sorted recommendations for unique items.
Description
Collaborative filtering systems can be used to recommend items to a user based on a user's previously expressed preferences. In general, a collaborative filter collects information about the preferences of many users and uses that information to predict the preferences of individual users. For example, if a user streams videos from a streaming video service, the service may utilize a collaborative filter to generate recommendations of alternate videos to stream based on an estimated likelihood that the user will be interested in the alternate videos. In another example, a user may purchase books from a bookseller, and the bookseller may utilize a collaborative filter to make recommendations to the user of alternate books based on an estimated likelihood that the user will be interested in the alternate books.
Collaborative filtering has limitations in its effectiveness, particularly when the relevant products or services are unique. Typically, a collaborative filter will assume that all similar items are identical. For example, when a user streams a particular movie, the collaborative filter assumes that all users who stream that movie view the same content, which is typically a valid assumption for a video streaming service. In another example, a collaborative filter that makes recommendations of books will typically assume that all customers that purchase a particular book are buying identical content. Accordingly, it can be advantageous to have systems, devices, and/or methods for measuring similarity of and generating recommendations for unique items.